Beyond Accuracy: Quantifying the Production Fragility Caused by Excessive, Redundant, and Low-Signal Features in Regression

Ali Nemati1 day ago25 sec read13 views

The experiment highlights issues with model complexity in machine learning: signal-to-noise ratio degradation, coefficient instability across retraining cycles, and increased sensitivity to data drift. It demonstrates that models incorporating many correlated or irrelevant features exhibit higher variability in learned coefficients and are more sensitive to changes in input distributions compared to leaner models using only high-signal features.

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Beyond Accuracy: Quantifying the Production Fragility Caused by Excessive, Redundant, and Low-Signal Features in Regression

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